Defect detecting apparatus, defect detecting method, information processing apparatus, information processing method, and program therefor

ABSTRACT

A defect detecting apparatus captures images of a protein chip formed on each die of a wafer at a plurality of different focal positions, with respect to every division region obtained by dividing each die in plurality; stores inspection target images for every division region at every focal position together with an ID for identifying each division region; creates a model image for every division region at every focal position by calculating an average luminance value of pixels of each inspection target image having the corresponding ID; extracts a difference between the model image and each inspection target image as a difference image; extracts a Blob having an area larger than a preset value from each difference image as a defect; and classifies the kind of the defect based on a feature point of the extracted Blob.

TECHNICAL FIELD

The present invention relates to a defect detecting apparatus capable ofdetecting a defect such as a foreign substance, a flaw, and the like byperforming a visual inspection of a microstructure such as MEMS (MicroElectro Mechanical Systems) formed on a semiconductor wafer, and alsorelates to a defect detecting method, an information processingapparatus, an information processing method, and a program to be usedtherefor.

BACKGROUND ART

Recently, MEMS devices, which integrate various functions in mechanical,electronic, optical and chemical field by using a micro-fabricationtechnology or the like, are attracting attention. As examples of MEMSdevice that have been in practical use so far, there are various sensorssuch as an acceleration sensor, a pressure sensor, an air flow sensor,and the like, which are used in an automobile or a medical field. Inparticular, MEMS devices are applied to a print head used in an inkjetprinter, a micro mirror array used in a reflective type projector, orother various actuators. Besides, MEMS devices are also used as, forexample, a protein analysis chip (so-called a protein chip), a DNAanalysis chip, or the like in the field of chemical synthesis,bio-analysis, or the like.

Meanwhile, since the MEMS devices are very fine microstructures, it isimportant to detect defects such as foreign substances, flaws or thelike present on the external appearances of the MEMS devices in amanufacturing process thereof. Conventionally, a visual inspection ofthe MEMS devices has been manually carried out by using a microscope.However, such inspection takes a lot of time and may cause adetermination error because the inspection is carried out with nakedeyes of an inspector.

Here, as an example of a technology for automating the visualinspection, disclosed in Patent Document 1 is a method for determiningwhether an inspection target object is normal or abnormal by capturingimages of a plurality of normal products among inspection target objectsby using, e.g., a CCD (Charge Coupled Device) camera or the like;storing them in a memory as a plurality of normal product images;calculating an average and a standard deviation of luminance values ofpixels at the same position on the respective normal product imagesafter performing position alignment of each normal product image; andthen comparing the calculated average and standard deviation of theluminance value with a luminance value of each pixel on an image of aninspection target object.

Further, disclosed in Patent Document 2 is a method for detecting apattern defect in a pattern inspection of a circuit board, a printedobject or the like, wherein the method involves the steps of: creatingreference image data to be used as a standard of normal products by wayof capturing images of a plurality of reference patterns, storing therespective reference pattern images, aligning the position of eachrespective reference pattern, performing a calculation for obtaining anaverage value or a median value between the respective image data forevery pixel, and creating the reference image data capable of being usedas a proper standard by excluding highly deviated data or abnormalvalues; and then comparing the reference image data with inspectiontarget image data.

-   Patent Document 1: Japanese Patent Laid-open Publication No.    2005-265661 (for example, FIG. 1)-   Patent Document 2: Japanese Patent Laid-open Publication No.    H11-73513 (for example, paragraph [0080])-   Patent Document 3: Japanese Patent Laid-open Publication No.    2000-180377 (for example, paragraphs [0010]˜[0028])

DISCLOSURE OF THE INVENTION Problems to Be Solved by the Invention

However, in the technologies disclosed in any one of Patent Documents 1and 2, the normal product image data or the reference image data used asthe inspection criteria (hereinafter, they are referred to as modelimage data) are created based on the images captured by photographing aplurality of normal products which is prepared separately from theinspection target images. Accordingly, prior to the creation of themodel image data, a process for determining and selecting a normalproduct is required to be performed, and since this process needs to beperformed manually, it takes much time and effort. Further, in theinspection of the microstructures such as the MEMS devices in which avery minute flaw or foreign substance is regarded as a defect, preparingan absolutely normal product (model) is difficult in the aspect ofmaintenance or management of model image data.

Moreover, in the technologies disclosed in Patent Documents 1 and 2, theimages of the inspection target objects such as the circuit boards orthe like are captured while they are mounted on a table individually.Thus, in case that there are individual variations, resulted from amanufacturing process, in the respective inspection target objects,there is a likelihood that such individual variations would be wronglydetected as defects, though they are not actual defects but individualvariations resulted from a manufacturing process. As a result,inspection accuracy would be deteriorated.

Further, if the inspection target object has a height (thickness) in thedirection of an optical axis of the image pickup camera, that is, if ithas a three-dimensional shape, accurate recognition of a defect may notbe possible even if the defect is present on the inspection targetobject because a captured image may become vague depending on an imagingfocus, resulting in a failure to detect the defect. In this regard,disclosed in Patent Documents 3 is an apparatus capable of detecting adefect or a foreign substance by capturing images of different heightpositions on a wafer surface by means of a plurality of optical systemshaving different focal positions.

In the technology described in Patent Document 3, however, a normalproduct image is previously stored as a reference image in case that theinspection target object is not a repetitive pattern. Therefore, thetechnology of Patent Documents 3 can not be applied to an inspection ofa device such as MEMS device for which obtaining a normal product imageis difficult.

In view of the foregoing, the present invention provides a defectdetecting apparatus capable of detecting a defect of a MEMS devicehaving the three-dimensional shape with high accuracy and efficiencywithout requiring an absolute model image, and also provides a defectdetecting method, an information processing apparatus, an informationprocessing method and a program to be used therefor.

Means for Solving the Problems

In accordance with one aspect of the present invention, there isprovided a defect detecting apparatus including: an imaging unit forcapturing images of a microstructure formed on each of a plurality ofdies on a semiconductor wafer at a first focal position and a secondfocal position different from the first focal position, with respect toevery division region obtained by dividing each die in plurality; astorage unit for storing therein the images of each division region atthe first and second focal positions together with identificationinformation for identifying a position of each division region withineach die as a first and a second inspection target image; a model imagecreating unit for creating an average image as a first and a secondmodel image for every identification information, the average imagebeing obtained by respectively averaging, among the first and secondinspection target images, the first and second inspection target imagesof respective division regions having the corresponding identificationinformation over the dies; and a detecting unit for detecting presenceor absence of a defect of the microstructure by comparing the first andsecond model images with the first and second inspection target imagescorresponding to the identification information of the first and secondmodel images, respectively.

Here, the microstructure refers to so-called MEMS (Micro ElectroMechanical Systems). The imaging unit is, for example, a camera providedwith an imaging device such as a CCD (Charge Coupled Device), a CMOS(Complementary Metal Oxide Semiconductor) sensor, or the like. Further,the kinds of the defect include, for example, a foreign substance, aflaw, a crack, and the like. Further, the second focal position does notnecessarily indicate only one focal point, but it is a concept includinga plurality of focal positions different from the first focal position.

With this configuration, it is possible to perform a high-accuracyvisual inspection of the microstructure although it is difficult toobtain an absolutely normal product model (sample) for creating themodel image based on the image of each division region in the target ofthe inspection.

Furthermore, by respectively creating the first and second model imagesfor every focal point by using the first and second inspection targetimages captured at different focal positions, it becomes possible todetect all existing defects with high accuracy even in case athree-dimensional MEMS device having a height in the direction of theoptical axis of the imaging unit is the target of the inspection.

In the defect detecting apparatus, the model image creating unit mayinclude a unit for calculating an average luminance value of every pixelincluded in each inspection target image having the correspondingidentification information.

In this way, by calculating the average luminance value for every pixelof each inspection target image, non-uniformity of the respective imagescan be effectively compensated, so that a high-quality model image canbe created and detection accuracy can be improved.

In the defect detecting apparatus, the imaging unit may successivelycapture the images of the microstructures on respective division regionshaving the corresponding identification information over the dies.

In this way, by predetermining the division regions on the same positionof the respective dies and capturing them successively, the model imageof each division region can be created efficiently and inspectionefficiency can be improved.

Further, after capturing the images of the microstructures in all thedivision regions on one die, the imaging unit may capture the images ofthe microstructures in respective division regions on another dieadjacent to the one die.

In this way, by successively capturing the image of each division regionon the same position of the respective dies, the model image of eachdivision region can be created efficiently and inspection efficiency canbe improved.

In the defect detecting apparatus, one or more second focal position mayexist along an image pickup direction, and the apparatus may furtherinclude: a unit for measuring a height of the microstructure along theimage pickup direction; and a unit for determining the number of thesecond focal positions based on the measured height.

In this way, since the number of the second focal positions can bedetermined according to the height of the microstructure, images ofdifferent microstructures can be captured while altering the number offocal positions, and defect detection is carried out at each focalposition. Accordingly, it is possible to detect all present defectsproperly regardless of the height of the microstructure.

In the defect detecting apparatus, the detecting unit may include a unitfor extracting a difference between the first model image and each firstinspection target image corresponding to the identification informationof the first model image and a difference between the second model imageand each second inspection target image corresponding to theidentification information of the second model image as a first and asecond difference image, respectively, and the defect detectingapparatus may further include a unit for generating and outputting acomposite difference image by composing the first and second differenceimages as a single image.

In this way, by detecting defects based on the first and secondinspection target images captured at the plurality of focal positionsand outputting the detection result as the single composite image, auser can check a defect at every focal position with naked eyes, so thata post-process after the defect detection can be allowed to progresssmoothly.

In the defect detecting apparatus, the microstructures are screeningtest vessels including: a plurality of recesses each having a thin filmshaped bottom surface and introducing therein a reagent and an antibodywhich cross-reacts with the reagent; and a plurality of holes providedin the bottom surface of each recess to discharge out the reagent whichdoes not react with the antibody.

Here, the vessel may be a protein chip. Accordingly, for example, acrack or flaw of the thin film (membrane) of the protein chip or aforeign substance adhered to the thin film can be detected with highaccuracy.

In this case, prior to respectively averaging of the first and secondinspection images corresponding to the identification information of thefirst and second model images, the model image creating unit mayrespectively align positions of the first and second inspection targetimages based on a shape of each recess of the vessel on the first andsecond inspection target images.

In this way, by using the shape of each recess of the vessel, anoverlapped position of the first and second inspection target images canbe accurately aligned, so that higher-quality model images can becreated. Further, specifically, the position alignment is carried out byvarying the relative position of each image by way of moving each imagealong X and Y directions or rotating it along θ direction.

Furthermore, in this case, the vessel may have a top surface distancedfrom the bottom surface by a preset interval, and the second focalposition may include a third focal position and a fourth focal positiondifferent from the third focal position, and the imaging unit may setthe top surface of the vessel as the first focal position, the bottomsurface as the third focal position and a preset position between thetop and bottom surfaces as the fourth focal position.

Accordingly, even in the event that defects are present at differentheight positions of the vessel, defects at each focal position can bedetected without missing any one of them by capturing each inspectiontarget image at the top surface, bottom surface and a preset positiontherebetween and creating the model image for every focal point. Here,the preset distance implies, for example, several hundreds ofmicrometers (μm), and the preset position refers to, for instance, amidway position between the top surface and the bottom surface. Here, itmay be also possible that there is a plurality of the fourth focalpositions.

In the defect detecting apparatus, the microstructure may be an electronbeam irradiation plate including a plate member having a plurality ofwindow holes for irradiating electron beams and a thin film provided tocover each window hole.

With this configuration, for example, a crack or a flaw of the thin film(membrane) of the electron beam irradiation plate or a foreign substanceadhered to the thin film can be detected with high accuracy.

In this case, prior to respectively averaging of the first and secondinspection images corresponding to the identification information of thefirst and second model images, the model image creating unit mayrespectively align positions of the first and second inspection targetimages based on a shape of each window hole of the electron beamirradiation plate on the first and second inspection target images.

In this way, by using the shape of each window hole of the electron beamirradiation plate, an overlapped position of the respective inspectiontarget images can be accurately aligned, so that higher-quality firstand second model images can be obtained.

Further, in this case, the electron beam irradiation plate may have atop surface on a side where the thin film is formed and a bottom surfacefacing the top surface with a preset interval maintained therebetween,and the imaging unit may capture an image of the electron beamirradiation plate by setting the top surface as the first focal positionand the bottom surface as the second focal position.

In this way, even in case that defects are present at different heightpositions of the electron beam irradiation plate, defects at each focalposition can be detected without missing any one of them by capturingeach inspection target image at the top surface and bottom surface ofthe electron beam irradiation plate and creating the model image forevery focal point. Here, the preset distance implies, for example,several hundreds of micrometers (μm)˜several millimeters (mm)

In accordance with another aspect of the present invention, there isprovided a defect detecting method including: capturing images of amicrostructure formed on each of a plurality of dies on a semiconductorwafer at a first focal position and a second focal position differentfrom the first focal position, with respect to every division regionobtained by dividing each die in plurality; storing the images of eachdivision region at the first and second focal positions together withidentification information for identifying a position of each divisionregion within each die as a first and a second inspection target image;creating an average image as a first and a second model image for everyidentification information, the average image being obtained byrespectively averaging, among the first and second inspection targetimages, the first and second inspection target images of respectivedivision regions having the corresponding identification informationover the dies; and detecting presence or absence of a defect of themicrostructure by comparing the first and second model images with thefirst and second inspection target images corresponding to theidentification information of the first and second model images,respectively.

In accordance with still another aspect of the present invention, thereis provided an information processing apparatus including: a storageunit for storing therein images of a microstructure formed on each of aplurality of dies on a semiconductor wafer as a first and a secondinspection target image, the images being captured at a first focalposition and a second focal position different from the first focalposition with respect to every division region obtained by dividing eachdie in plurality, together with identification information foridentifying a position of each division region within each die; a modelimage creating unit for creating an average image as a first and asecond model image for every identification information, the averageimage being obtained by respectively averaging, among the first andsecond inspection target images, the first and second inspection targetimages of respective division regions having the correspondingidentification information over the dies; and a detecting unit fordetecting presence or absence of a defect of the microstructure bycomparing the first and second model images with the first and secondinspection target images corresponding to the identification informationof the first and second model images, respectively.

Here, the information processing apparatus may be, for example, acomputer such as a PC (Personal Computer), and it may be of a so-callednotebook type or desktop type.

In accordance with still another aspect of the present invention, thereis provided an information processing method including: storing imagesof a microstructure formed on each of a plurality of dies on asemiconductor wafer as a first and a second inspection target image, theimages being captured at a first focal position and a second focalposition different from the first focal position with respect to everydivision region obtained by dividing each die in plurality, togetherwith identification information for identifying a position of eachdivision region within each die; creating an average image as a firstand a second model image for every identification information, theaverage image being obtained by respectively averaging, among the firstand second inspection target images, the first and second inspectiontarget images of respective division regions having the correspondingidentification information over the dies; and detecting presence orabsence of a defect of the microstructure by comparing the first andsecond model images with the first and second inspection target imagescorresponding to the identification information of the first and secondmodel images, respectively.

In accordance with still another aspect of the present invention, thereis provided a program for executing, in an information processingapparatus, the processes of: storing images of a microstructure formedon each of a plurality of dies on a semiconductor wafer as a first and asecond inspection target image, the images being captured at a firstfocal position and a second focal position different from the firstfocal position with respect to every division region obtained bydividing each die in plurality, together with identification informationfor identifying a position of each division region within each die;creating an average image as a first and a second model image for everyidentification information, the average image being obtained byrespectively averaging, among the first and second inspection targetimages, the first and second inspection target images of respectivedivision regions having the corresponding identification informationover the dies; and detecting presence or absence of a defect of themicrostructure by comparing the first and second model images with thefirst and second inspection target images corresponding to theidentification information of the first and second model images,respectively.

Effect of the Invention

In accordance with the present invention as described above, highlyaccurate and efficient detection of a defect of a MEMS device having athree-dimensional shape can be realized without having to use anabsolute model image.

BEST MODE FOR CARRYING OUT THE INVENTION

Hereinafter, embodiments of the present invention will be described indetail with reference to the accompanying drawings.

FIG. 1 is a configuration view of a defect detecting apparatus inaccordance with an embodiment of the present invention. As illustratedin FIG. 1, the defect detecting apparatus 100 includes a wafer table 2for holding thereon, e.g., a silicon semiconductor wafer 1 (hereinafter,simply referred to as a wafer 1); an XYZ stage 3 for moving the wafertable 2 along X, Y and Z directions of the figure; a CCD camera 6 forcapturing an image of the wafer 1 from above; a light source 7 forilluminating the wafer 1 while the CCD camera 6 captures the image; animage processing PC (Personal Computer) 10 for controlling the operationof each component and performing image processing to be described later.

The wafer 1 is transferred onto the wafer table 2 by a non-illustratedtransfer arm or the like and is attracted to and fixed on the wafertable 2 by, for example, an adsorption unit such as a non-illustratedvacuum pump or the like. Further, it may be also possible not to attractthe wafer 1 onto the wafer table 2 directly but to prepare a separatetray (not shown) capable of holding the wafer 1 thereon and to attractand hold the tray instead while the wafer 1 is kept on the tray. As willbe described later, in case that holes are formed in the wafer 1, forexample, it may be difficult to vacuum-attract the wafer 1 directly. Insuch case, the adsorption method using the tray would be effective. Onthe wafer 1, there is formed a protein chip as a MEMS device. The defectdetecting apparatus 100 is an apparatus for detecting a defect such as aforeign substance or flaw on the protein chip which is an inspectiontarget object. Detailed explanation of the protein chip will be providedlater.

The CCD camera 6 is fixed at a predetermined position above the wafer 1,and it is provided with a lens, a shutter (not shown), or the like.Based on a trigger signal outputted from the image processing PC 10, theCCD camera 6 captures an image of the protein chip, which is formed at apredetermined portion of the wafer, under the light emitted from thelight source 7 while enlarging the image by the provided lens, and sendsthe captured image to the image processing PC 10. Further, the XYZ stage3 varies the relative distance between the CCD camera 6 and the wafer 1by moving the wafer 1 in vertical direction (Z direction), whereby afocal position can be varied when the CCD camera 6 takes the image ofthe wafer 1. Further, it may be also possible to vary the focalpositions by means of moving the CCD camera 6 along the Z directioninstead of moving the XYZ stage 3.

Further, the lens of the CCD camera 6 is made up of a zoom lens, and itcan capture images of the protein chip in different magnifications byvarying a focal distance. In the present embodiment, the magnificationof the CCD camera is variable between two levels: about 7 times (lowmagnification) and about 18 times (high magnification). In case of thelow magnification, the size of view is, e.g., about 680×510 (μm²); andin case of the high magnification, the size of view is, e.g., about270×200 (μm²). However, the magnifications are not limited to theseexamples. Further, instead of the CCD camera 6, a camera provided withanother type of imaging device such as a CMOS sensor can be used.

The light source 7 is fixed at a predetermined position above the wafer1, and it includes, for example, a flash lamp made up of ahigh-luminance white LED or a Xenon lamp, a flash turn-on circuit forcontrolling the lightening of the flash lamp, and so forth. The lightsource 7 illuminates the predetermined portion of the wafer 1 byemitting light of high luminance during a preset period of time, e.g.,for about several μ seconds, based on a flash signal outputted from theimage processing PC 10.

The XYZ stage 3 includes a motor 4 for moving an X stage 11 and a Ystage 12 in X, Y and Z directions along a movement axis 13; and anencoder 5 for determining moving distances of the X and Y stages 11 and12. The motor 4 may be, for example, an AC servo motor, a DC servomotor, a stepping motor, a linear motor, or the like, and the encodermay be, for instance, one of various kinds of motor encoders, a linearscale, or the like. Whenever the X stage 11 and the Y stage 12 are movedas much as a unit distance along the X, Y and Z directions, the encoder5 generates an encoder signal as movement information (coordinateinformation) indicating the movements, and outputs the generated encodersignal to the image processing PC 10.

The image processing PC 10 receives the encoder signal inputted from theencoder 5, and outputs a flash signal to the light source 7 based on thereceived encoder signal, and also outputs a trigger signal to the CCDcamera 6. Further, based on the encoder signal inputted from the encoder5, the image processing PC 10 also outputs a motor control signal to themotor 4 to control the operation of the motor 4.

FIG. 2 sets forth a block diagram for illustrating the configuration ofthe image processing PC 10. As illustrated in FIG. 2, the imageprocessing PC 10 includes a CPU (Central Processing Unit) 21, a ROM(Read Only Memory) 22, a RAM (Random Access Memory) 23, an input/outputinterface 24, a HDD (Hard Disk Drive) 25, a display unit 26 and amanipulation input unit 27, and the respective components are connectedwith each other via an internal bus 28.

The CPU 21 controls the whole operation of each component of the imageprocessing PC 10 and performs various operations in the image processingto be described later. The ROM 22 is a non-volatile memory for storingtherein programs required for driving the image processing PC 10, othervarious data or programs requiring no update, or the like. The RAM 23 isa volatile memory used as a working area of the CPU 21, and it functionsto read out various data or programs from the HDD 25 or the ROM 22 andstore them temporarily therein.

The input/output interface 24 is an interface for connecting themanipulation input unit 27, the motor 4, the encoder 5, the light source7 and the CCD camera 6 with the internal bus 28 to perform an input ofan operation input signal from the manipulation input unit 27 and anexchange of various signals with respect to the motor 4, the encoder 5,the light source 7 and the CCD camera 6.

The HDD 25 stores, in an embedded hard disk, an OS (Operation System),various programs for performing an image pickup process and an imageprocessing to be described later, other various applications, image datasuch as the image of the protein chip as the inspection target imagecaptured by the CCD camera 6 and a model image (to be described later)created from the inspection target image, various data to be used asreference in the image pickup process and the image processing, and soforth.

The display unit 26 includes, for example, a LCD (Liquid CrystalDisplay), a CRT (Cathode Ray Tube), or the like, and displays the imagecaptured by the CCD camera 6 or various condition screens for the imageprocessing. The manipulation input unit 27 includes, for example, akeyboard, a mouse, or the like, and inputs a manipulation from a user inthe image processing or the like to be described later.

Now, the protein chip formed on the wafer 1 will be explained. FIG. 3 isa top view of the wafer 1. As illustrated in the figure, for example, 88semiconductor chips 30 (hereinafter, simply referred to as chips 30 ordies 30) are formed on the wafer 1 in a grid pattern. Here, it should benoted that the number of the dies 30 is not limited to 88.

FIG. 4 is a top view showing one of the dies 30 of the wafer 1. As shownin FIG. 4, a protein chip 35 having a plurality of circular recesses 50on its entire surface is formed on each die 30. Each die 30, i.e., eachprotein chip 35 has an approximately square shape, and the length s ofone side thereof is within a range of, for example, about severalmillimeters (mm) to several tens of millimeters (mm). However, thedimension of the length is not limited to this example.

FIG. 5 shows enlarged views of one recess 50 of the protein chip 35.FIG. 5( a) is a top view of the recess 50, and FIG. 5( b) is aZ-directional cross sectional view of the recess 50.

As illustrated in FIG. 4 and FIG. 5, a thin film (membrane) 53 having aplurality of holes 55 is formed in a bottom surface 52 of each recess 50of the protein chip 35. The holes 55 are densely formed over the entirecircular bottom surface 52 of the recess 50 in same shapes. The diameterd1 of each recess 50 is, for example, hundreds of micrometers (μm), andthe diameter d2 of each hole 55 is, for example, several micrometers(μm). Further, the depth (height from a top surface 51 to the bottomsurface 52) h of the recess 50 is, for example, several hundreds ofmicrometers (μm). Here, it should be noted that these dimensions are notlimited to the examples.

The protein chip 35 is a silicon vessel for mounting a carrier e.g.,latex fine particles (latex beads) on the bottom surface 52 of therecess 50 and screening protein having a specific property of beingadsorbed with the latex beads by antibody cross-reaction when anantibody (protein) is injected into the recess 50 as a reagent. Thereagent (protein) which is not adsorbed with the latex beads isdischarged out through the holes 55 on the bottom surface 52, so thatonly the protein having the specific property remains in the recess 50.

Here, a manufacturing method of the protein chip 35 will be explainedsimply. First, the thin film 53 such as a silicon oxide film or the likeis formed on one side of the wafer 1 by a CVD (Chemical VaporDeposition) method. Then, photoresist is coated on the other side of thewafer 1, and after removing unnecessary portions by a photolithographytechnique, etching is performed by using a resist pattern as a mask,whereby the plurality of recesses 50 are formed on the wafer 1 while thethin film 53 still remains. Then, photoresist is coated on the thin film53 of each recess 50, and portions of the photoresist corresponding tothe holes are removed by the photolithography technique, and etching isperformed by using a resist pattern as a mask. As a result, the proteinchip 35 having the plurality of recesses 50 each having the thin film 53provided with a plurality of holes 55, as illustrated in FIG. 5, can beobtained.

Hereinafter, an operation of the defect detecting apparatus 100 inaccordance with the embodiment of the present invention, for detecting adefect of the protein chip 35 will be described. FIG. 6 provides aschematic flowchart to describe the operation of the defect detectingapparatus 100 until it detects a defect.

As described in FIG. 6, the CCD camera 6 first captures an image of eachdie 30, on which the protein chip 35 is formed, with the lowmagnification (step 101). To elaborate, each die is divided into, e.g.,18×13 (a total of 234) first division regions 71 as illustrated in FIG.7, and an image of each division region 71 is obtained by the CCD camera6 under the light of the light source 7. Here, the number and the aspectratio of the first division regions 71 are not limited to the mentionedexamples. Each of the first division regions 71 is previously assignedan ID for identifying its location, and the HDD 25 of the imageprocessing PC 10 stores therein each ID. Based on these IDs, the imageprocessing PC 10 can identify the first division regions 71 located atthe same positions on different dies 30. Further, each die 30 is alsoassigned an ID, so that the image processing PC 10 can determine whichone of the dies 30 each first division region 71 belongs to.

At this time, as described above, the image processing PC 10 outputs amotor driving signal to the motor 4 based on an encoder signal from theencoder 5, thereby moving the XYZ stage 3. Further, the image processingPC 10 also generates a trigger signal and a flash signal based on theencoder signal, and outputs the generated trigger signal and flashsignal to the CCD camera 6 and the light source 7, respectively.

Each time the XYZ stage 3 is moved, the light source 7 emits lighttoward the protein chip 35 for every several micro (μ) seconds based onthe flash signal, and under the light, the CCD camera 6 successivelycaptures images of the respective first division regions 71 of theprotein chip 35 on the wafer 1 at a speed of, e.g., about 50sheets/second, based on the trigger signal.

FIG. 8 illustrates trajectories of image pickup positions when the CCDcamera 6 captures the images of the respective first division regions 71of the protein chip 35. In the present embodiment, two image pickuppaths can be considered, as illustrated in FIG. 8.

As shown in FIG. 8( a), among the 88 dies 30 of the wafer 1, the CCDcamera 6 starts to capture an image from, e.g., the leftmost die 30among the dies 30 of which Y-coordinate values are maximum, and aftersuccessively capturing the images of all of the 18×13 first divisionregions 71 of the leftmost die 30, e.g., line by line, the CCD camera 6proceeds to the next die 30 and captures images of all first divisionregions 71 thereon line by line again.

That is, the image processing PC 10 outputs the motor driving signal tothe motor 4 such that the image pickup position for each first divisionregion 71 of one die 30 starts from, e.g., the first division region 71belonging to the uppermost line and the leftmost row and is then movedrightward along the X direction, and if it reaches the rightmost end,the image pickup position is moved along the Y direction by one line andthen is moved leftward along the X direction, and also if it reaches theleftmost end, the image pickup position is moved by one line along the Ydirection again and then is then moved rightward along the X directionon the next line. If the image pickup of all the first division regions71 of one die 30 is completed by repeating the above-mentionedprocedure, the image pickup position is moved to the adjacent next die30, and the same movements are repeated. At this time, since theposition of the CCD camera 6 is fixed, in fact, the XYZ stage 3 is movedalong the opposite directions to those of the trajectory shown in FIG.8( a). The CCD camera 6 successively captures the images of each firstdivision region 71 based on the trigger signal outputted from the imageprocessing PC 10 while keeping up with such movements.

Further, as illustrated in FIG. 8( b), the image pickup position canalso be moved such that the CCD camera 6 successively captures theimages of first division regions having the corresponding ID (located atthe same positions) on the different dies 30.

That is, the image processing PC 10 sets the image pickup position ofthe CCD camera 6 to start from the leftmost die 30 among the dies 30 ofwhich Y-coordinate values are maximum, for example, and drives the motor4 such that the image pickup position of the CCD camera 6 is moved alongX and Y directions to allow the CCD camera 6 to first pass throughrespective first division regions 71 (first division regions 71 markedby black circles) having the corresponding ID on the different dies 30and having minimum X-coordinate values while having the maximumY-coordinate values and then to pass through respective first divisionregions 71 (first division regions 71 marked by white circles) havingthe corresponding ID and located next to the first image pickuppositions along the X direction, and then to repeat the movements ofallowing the CCD camera 6 to pass through respective first divisionregions 71 located on the same positions on the different dies 30.Keeping up with such movements, the CCD camera 6 repeats the operationof successively capturing the images of the plurality of first divisionregions 71 having the corresponding ID for every die 30, based on thetrigger signal outputted from the image processing PC 10.

The image processing PC 10 allows the CCD camera 6 to perform the imagepickup operation by selecting one of the two image pickup paths whichallows shorter image pickup time. In case that the image pickup pathshown in FIG. 8( a) is selected, each image pickup interval between thefirst division regions 71, that is, the movement interval of the XYZstage 3 is the same as each interval between the first division regions71 whereas in case that the image pickup path shown in FIG. 8( b) isselected, the movement interval of the XYZ stage 3 is the same as eachinterval between the dies 30. Accordingly, the CPU 21 of the imageprocessing PC 10 is capable of calculating a driving speed of the motor4 based on these movement intervals and the image pickup frequency ofthe CCD camera 6. By multiplying the entire image pickup path shown inFIG. 8( a) or 8(b) determined by the layout of the dies 30 shown in FIG.3 with this driving speed, the image pickup time that would be taken forcapturing the images of the first division regions 71 of all the dies 30can be estimated for each of the cases of FIG. 8. By comparing the imagepickup times in both cases, the image processing PC 10 determines whichone of image pickup paths shown in FIG. 8 will take shorter image pickuptime, and selects the image pickup path that requires shorter imagepickup time.

The images of the first division regions 71 captured by the CCD camera 6are transmitted to the image processing PC 10 as inspection targetimages along with their IDs for identifying the first division regions71, and are stored in the HDD 25 or the RAM 23 via the input/outputinterface 24 of the image processing PC 10. Further, in the presentembodiment, though the size of the inspection target images captured bythe CCD camera 6 is a so-called VGA (Video Graphics Array) size (640×480pixels), the present invention is not limited to that size.

In the present embodiment, as the XYZ table 3 is moved along the Zdirection, the distance between the protein chip 35 of the wafer 1 andthe CCD camera 6 can be varied by the movement of the XYZ stage 3 asdescribed above, thus enabling to capture images of the inspectiontarget images at different focal positions. FIG. 9 provides theillustration of such operation.

As shown in FIG. 9, the XYZ stage 3 is moved in an upward direction (Z1direction of FIG. 9) and in a downward direction (Z2 direction of FIG.9) based on a focus signal from the image processing PC 10, thus varyingthe distance between the CCD camera 6 and the protein chip 35 in, forexample, three levels (focal points F1 to F3). That is, as the XYZ stage3 is moved along the Z2 direction, the CCD camera 6 focuses on the topsurface 51 of the protein chip 35 (focal point F1), and as the XYZ stage3 is further moved along the Z1 direction from there, the CCD camera 6focuses on an approximately midway position between the top surface 51and the bottom surface 52 of the protein chip 35 (focal point F2), andas the XYZ stage 3 is moved along the Z1 direction, the CCD camera 6 canfocus on the bottom surface 52 of the protein chip 35 (focal point F3).Further, the number of the variable focal points is not limited to onlythree.

As described above, the defect detecting apparatus 100 in accordancewith the present embodiment captures the inspection target images at theplurality of different focal points. Accordingly, even in case theinspect target object has a three-dimensional shape having thickness(depth or height) in the Z direction as in the case of the protein chip35 in the present embodiment, it is possible to capture images atrespective Z-directional positions and thus avoid failure to detect thedefect. The CCD camera 6 classifies the images captured at each focalposition through the path of FIG. 8( a) or 8(b) based on their focalpositions, and then transmits them to the image processing PC 10. Theimage processing PC 10 identifies the images as the inspection targetimages for every focal position and then stores them in the HDD 25 orthe RAM 28. That is, as described above, in case that the focal pointsare three, F1 to F3, the CCD camera 6 performs the image pickup processfor every focal position by repeating three times of movements, alongthe image pickup path of FIG. 8( a) or 8(b).

Referring back to the flowchart of FIG. 6, while carrying out theabove-described image pickup process by the CCD camera 6, the CPU 21 ofthe image processing PC 10 also performs a filtering process by means ofa high pass filter for each acquired inspection target image every timethe inspection target image is obtained from the CCD camera 6 (step102).

The protein chip 35 in the present embodiment has the thin film 53 atthe bottom surface 52 thereof. In the event that the thin film 53 isbent, for example, non-uniformity of luminance may be resulted due to aflatness of the thin film 53. Further, the non-uniformity of luminancemay be caused due to, e.g., a deviation of an optical axis of the CCDcamera 6 or a difference in degree of uniformity on the side where thelight from the light source 7 reaches, or the like. Such luminancevariability may be extracted as a difference in a difference extractingprocess to be described later, leading to erroneous defect detection.

A non-uniform luminance portion is a portion of the inspection targetimage where luminance is gradually varied. That is, the non-uniformluminance component can be referred to as a low frequency component.Here, in the present embodiment, this low frequency component is removedfrom each inspection target images by means of the high pass filter.

FIG. 10 presents a flowchart to describe the high pass filtering processin detail. As shown in FIG. 10, the CPU 21 of the image processing PC 10reads out a duplicate of the inspection target image from the HDD 25 tothe RAM 23 (step 61), and performs a Gaussian blurring process on theinspection target image (step 62). Further, though a set value forblurring is, for example, about 15 to 16 radius pixels, the presentinvention is not limited thereto.

In this Gaussian blurring process, since a pixel of a high frequencycomponent (e.g., an edge portion) in the original inspection targetimage is blurred by a contribution from neighboring pixels of a lowfrequency component, a high blurring effect can be obtained. Meanwhile,as for the pixel of low frequency component (e.g., the non-uniformluminance portion) in the original inspection target image, neighboringpixels contributed thereto is also a low frequency component, so thatthe blurring effect is low, and a change from the original inspectiontarget image can be hardly observed. Accordingly, an output image(hereinafter, referred to as a “Gaussian blur image”) obtained by theGaussian blurring process is an image having low frequency componentsleft after high frequency components in the original inspection targetimage are smoothed.

Subsequently, the CPU 21 subtracts the Gaussian blur image from theoriginal inspection target image (step 63). By subtracting, from thehigh frequency components of the original inspection target image, theircorresponding low frequency components of the Gaussian blur image, onlythe original high frequency components are left. Further, bysubtracting, from the low frequency components of the originalinspection target image, their corresponding low frequency components ofthe Gaussian blur image, the original low frequency components areremoved. That is, the image obtained by the subtraction process is animage having only the high frequency components left after removing thelow frequency components from the original inspection target image. TheCPU 21 updates the original inspection target image with the imageacquired after the subtraction process and stores it in the HDD 25 (step64).

Referring back to FIG. 6, the CPU 21 determines whether to perform animage pickup process of each inspection target image for every firstdivision region 71 and whether to perform the filtering process with thehigh pass filter for every inspection target image (steps 103 and 104).If it is determined that the image pickup process of all the inspectiontarget images and the filtering process therefor are performed (Yes), aprocess for creating a model image for each division region is performedby using the inspection target images after the filtering process (step105). Further, in the present embodiment, though the image pickupprocess of the inspection target image and the high pass filteringprocess are performed in a parallel manner, it may be also possible toperform the high pass filtering process after completing the imagepickup process of the inspection target for all of the first divisionregions 71 (that is, it may possible that a process of step 102 and step103 are reversed in sequence).

Here, the process for creating the model image will be explained indetail. FIG. 11 provides a flowchart to describe a process sequenceuntil the image processing PC 10 creates the model image, and FIG. 12schematically illustrates the way in which the image processing PC 10creates the model image.

As shown in FIG. 11, the CPU 21 of the image processing PC 10 reads outinspection target images, which have the corresponding ID over the dies30 among the inspection target images after the high pass filteringprocess, from the HDD 25 to the RAM 23 (step 41), and performs positionalignment of each read-out inspection target image (step 42).Specifically, among the inspection target images of the first divisionregions 71 present at the same position on the different dies 30, theCPU 21 recognizes, for example, the shapes of edge portions on therecesses 50 of the protein chips 35 and carries out the positionalignment by controlling shifts in the X and Y directions and rotationsin the θ direction to allow those shapes to be overlapped between therespective inspection target images.

For example, as illustrated in FIG. 12, the CPU 21 reads out inspectiontarget images 40 a to 40 f, having the corresponding ID, captured forfirst division regions 71 a present at the same position on thedifferent dies 30. In the present embodiment, since the number of thedies 30 is 88, the total number of inspection target images 40 havingthe corresponding ID becomes 88, too. The CPU 21 overlaps all of the 88inspection target images 40 together and aligns their positions based onthe shapes of the recesses 50 or the like. As described, by performingthe position alignment based on the shapes of the recesses 50 or thelike, easy and exact position alignment can be realized.

Subsequently, in the state that the above-described position alignmentis performable, the CPU 21 calculates an average pixel luminance valuefor every pixel on the same position among the respective inspectiontarget images 40 (step 43). Upon the completion of the calculation ofthe average luminance values of all the pixels in each inspection targetimage 40 of the first division region 71 a (Yes in step 44), based onthe calculation result, the CPU 21 generates an image made up of pixelshaving these average luminance values as a model image 45, and stores itin the HDD 25 (step 45).

By repeating this process, the CPU 21 determines whether the model image45 is created for each of the corresponding first division regions 71between the dies 30 (step 46), and if it is determined that all themodel images 45 are created (Yes), the process is finished.

By the above-described process, it is possible to create the modelimages 45 based on the actual inspection target images 40 even in theinspection of the MEMS devices for which acquisition of an absolutenormal product sample is impossible. There is a likelihood that a defectsuch as a foreign substance, a flaw, a crack of thin film and the likemay be present on each inspection target image 40. However, by dividingeach die 30 into a multiplicity of (in the present embodiment, 234)first division regions 71 and calculating the average luminance valuesover the plurality of (in the present embodiment, 88) dies 30, thedefect of each inspection target image 40 can be compensated, and itbecomes possible to create the substantially ideal model images 45.Thus, highly accurate defect detection is enabled.

As described above, since each of the inspection target image 40 at asingle first division region 71 is present for each of the focal pointsF1 to F3, the model image 45 is also created for each focal point.Accordingly, in the present embodiment, since the number of the firstdivision regions 71 is 234 on each die 30, 234×3 (a total of 702) sheetsof model images are created.

Referring back to the flowchart of FIG. 6, after the completion ofcreation of the model images 45, the CPU 21 performs a process forextracting a difference between the model images 45 and each inspectiontarget image 40 after the high pass filtering process for every firstdivision region 71 (step 106).

To elaborate, as in the case of the above-stated position alignmentprocess for the creation of the model images 45, the CPU 21 performsposition alignment along the X, Y, and θ directions based on the shapesof the recesses 50 present on the model images 45 and each inspectiontarget image 40, and performs a binarization process by extracting thedifference between the two images by a subtraction process and thenoutputs the result as a difference image.

Then, the CPU 21 performs filtering by a so-called Blob extraction forthis difference image (step 107). Here, a Blob implies a cluster ofpixels having a preset (or a preset range of) gray scale value on thedifference image. From the difference image, the CPU 21 extracts only aBlob larger than a certain area (e.g., 3 pixels) among Blobs.

FIG. 13 shows difference images before and after the Blob extractingprocess. FIG. 13( a) illustrates a difference image 60 before the Blobextraction, and FIG. 13( b) illustrates a difference image after theBlob extraction (hereinafter referred to as a “Blob extraction image65”).

In FIG. 13( a), conspicuous white portions indicate differences betweenthe model image 45 and the inspection target image 40. In thisdifference image 60, a process for enhancing luminance value as much as,e.g., about 40 times the luminance value of the original differenceimage is performed to emphasize the differences. As shown in FIG. 13(a), besides the defect such as the foreign substances, the flaws, andthe like, the difference image 60 before the Blob extraction also hasmicroscopic noises 84 observed as portions surrounded by white dashedlines due to various reasons such as contamination of the lens 14 of theCCD camera 6, the degree of uniformity of the illumination of the lightsource 7, and so forth. Since the presence of the noises 84 leads towrong detection of defects, these noises 84 need to be eliminated.

The noises 84 have a smaller area than the area of defects such asforeign substances or flaws. Here, as illustrated in FIG. 13( b), thenoises 84 can be eliminated by performing a filtering process forextracting only a Blob larger than a preset area by way of removing aBlob smaller than the preset area. By this Blob extracting process, onlythe cracks 81 of the thin film of the recess 50 of the protein chip 35or foreign substances 82 such as dirt adhered to the protein chip 35 canbe extracted from the Blob extraction image 65. At this time, the CPU 21recognizes them just as defect candidates without determining the kindsof the defects such as the foreign substances, cracks, flaws, and thelike.

Subsequently, referring back to the flowchart of FIG. 6, when a defectcandidate is detected by the Blob extracting process (Yes in step 108),the CPU 21 determines whether it is necessary to capture ahigher-magnification image of the protein chip 35 from which the defectcandidate is detected (step 109). That is, the CPU 21 determines whetherthere has been inputted a user manipulation for instructing a pickup ofa more detailed higher-magnification image of the first division region71 to which the inspection target image 40 containing the defectcandidate belongs. When it is determined that the pickup ofhigher-magnification image is necessary (Yes), the CCD camera 6 capturesa high-magnification image of each of second division regions 72 in thefirst division region 71 from which the defect candidate is detected andthe other first division regions 71 having the ID corresponding to thatof this first division region 71 on the different dies 30, wherein thesecond division regions are obtained by dividing each first divisionregion in a smaller unit (step 113).

In a defect classifying process to be described later, thoughdetermination of the defect and classification thereof are carried outbased on the area of the extracted Blob, for example, it may beimpossible to calculated the Blob area accurately in case that the Blobextraction image 65 is created based on the inspection target imagecaptured with a low magnification. Further, with the low-magnificationimage, it can be deemed that an accurate shape of the defect may not berecognized and thus an exact classification of the defect may not beachieved. In the present embodiment, however, by capturing thehigher-magnification image of the protein chip 35, determination of thedefect and classification thereof, which will be described later, areenabled to be carried out accurately.

FIG. 14 schematically illustrates the high-magnification image pickup ofeach second division region 72 in the first division region 71 fromwhich the defect candidate is detected. As can be seen from FIG. 14, incase that the defect candidate is detected from the inspection targetimage capturing a first division region 71 a on a certain die 30, thisfirst division region 71 a is further divided into 3×3 (9 in total)second division regions 72. Further, on the other dies 30, the firstdivision regions 71 having the ID corresponding to that of the firstdivision region 71 a are also further divided into second divisionregions 72. Like each first division region 71, each second divisionregion 72 is assigned an ID for identifying its position on each die 30.

The CCD camera 6 captures an image of each second division region 72 inthe same size (VGA size) as the first division region 71. That is, theCCD camera 6 captures the image of the second division region 72 byenlarging it three times as larger as that of the first division region71. Each captured image is stored in, e.g., the HDD 25 of the imageprocessing PC 10 as an inspection target image along with the ID of eachsecond division region.

Moreover, as for an image pickup path for each second division region 72of each die 30, a shorter path among those shown in FIG. 8 is selected,as in the case of the image pickup of the first division region 71. Thatis, the CPU 21 determines which path is shorter among a path throughwhich the image pickup is first carried out for all the second divisionregions 72 of the first division region 71 of one die 30 and thencarried out for each second division region 72 of the first divisionregions 71 corresponding to that of the different dies 30 and a paththrough which the image pickup is carried out in the sequence of thesecond division regions 72 having the corresponding ID among thecorresponding first division regions 71 over the dies 30, and thenperforms the image pickup process through either shorter one of the twopaths.

If the image pickup for the first division region 71 found to have thedefect candidate and the second division regions 72 of the firstdivision region corresponding thereto is completed (step 113), the CPU21 performs a filtering process with the high pass filter (step 114) anda model image creating process (step 117) on each inspection targetimage, as in the processes of steps 102 to 107, and conducts adifference extracting process between a model image and each inspectiontarget image captured on each second division region 72 of the firstdivision region 71 from which the defect candidate is detected (step118). The CPU then performs a filtering process by Blob extraction (step119).

Further, since the inspection target image of each second divisionregion 72 is captured with a higher resolution than that of theinspection target image of the first division region 71, a thresholdvalue (pixel) of an Blob area extracted by the Blob extracting processin step 118 is set to be larger than a threshold value of the Blob areaextracted by the Blob extracting process for each first division region71 in step 107. However, it should be noted that there is no differencein the actual Blob area (μm), converted from the threshold value(pixel), on the protein chip 35.

FIG. 15 illustrates Blob extraction images 65 extracted from eachinspection target image of the first division region 71 and the seconddivision region 72. FIG. 15( a) shows a Blob extraction image 65 aextracted from the first division region 71, and FIG. 15( b) shows aBlob extraction image 65 b extracted from the second division region 72.

As can be seen from FIG. 15, in the Blob extraction image 65 a of thefirst division region 71 a obtained in step 107, a region viewed as aforeign substance 82 is conspicuously observed at a left lower endportion. However, since its area is small, it is difficult to calculatean exact value of area. Therefore, as shown in FIG. 15( b), by dividingthe first division region 71 into 9 second division regions 72, and thencapturing the high-magnification images of the second division regions72 in which the foreign substance 82 is observed, that foreign substance82 can be displayed with the high resolution, so that accuratecalculation of its areas is enabled.

Further, in case that the defect candidate is extracted in step 108, itmay be also possible to perform the high-magnification image pickupautomatically without carrying out the process of determining thenecessity for the high-magnification image pickup in step 109. Moreover,if the performance of the image processing PC 10, the motor 4 and theencoder 5 is excellent and the processing time is within an allowablerange, it may be possible to create the model image 45 for all thesecond division regions 72 by capturing images of the second divisionregions 72 of all the first division regions 71 on every die 30 as wellas the first division region 71 from which the defect candidate isextracted. In such case, it would be desirable to perform the imagepickup process, the high pass filtering process and the model imagecreation process for every second division region 72 immediately afterthe completion of the Blob extracting process for the first divisionregion 71 without determining the necessity of the high-magnificationimage pickup in step 109 and to perform the Blob extracting process foreach second division region 72 of the first division region 71 which isdetermined to contain the detected defect candidate by the CPU 21.

Referring back to the flowchart of FIG. 6, when it is determined in step109 that the high-magnification image pickup is not necessary (No) orwhen the Blob extracting process from the second division regions 72 insteps 113 to 119 is completed, the CPU 21 performs classification of thedefect candidate shown in the Blob extraction image 65 (step 110).

That is, for each Blob marked white in the Blob extraction image 65, CPU21 determines whether each Blob is a defect or not based on featurepoints such as its area, circumferential length, noncircularity, anaspect ratio and the like, and classifies the kind of the defect bydetermining whether the defect is a foreign substance, a flaw, a crack,or the like.

Specifically, the image processing PC 10 collects sample images of everykind of defects such as foreign substance, flaws, cracks and the likeand stores feature point data in the HDD 25 or the like as a featurepoint database, and compares feature points detected from each Blob ofthe Blob extraction image 65 of the inspection target with the storedfeature point data.

For example, one side of the foreign substance in the present embodimentranges about several micrometers to tens of micrometers, and the lengthof the flaw range from about several micrometers to hundreds ofmicrometers. Further, when the foreign substance is compared with theflaw, the flaw has an aspect ratio with a very elongated width orlength, and its circumferential length is also lengthened. Further,though a crack of the thin film is generated at the edge portion of therecess 50 in a curved shape, the noncircularity of the recess 50 isincreased in comparison with a normal case. The image processing PC 10stores these data as the feature point data, and carries out theclassification of the defect by the comparison of the respective featurepoints of the detected Blob with the stored data.

Further, as described above, the protein chip 35 in the presentembodiment has the holes of, e.g., several micrometers in the thin film53 at the bottom surface of the recess 50, and the holes 55 serve todischarge the reagent. Accordingly, even when a foreign substance isadhered inside the recess 50, the foreign substance would be dischargedthrough the holes 55 along with the reagent in case that the foreignsubstance has a diameter smaller than that of the holes 55 of severalmicrometers, thus causing no problem in screening using the protein chip35. Thus, the diameter of the holes 55 will be set as a threshold valuefor the foreign substances, and a foreign substance having a smallerdiameter than that will not be considered as a defect. Meanwhile, for aflaw or a crack, since the reagent leaks therefrom, normal screeningcannot be performed. For the reason, the flaw or crack will be alwaysconsidered as a defect.

As stated above, in the event that the feature points of the Blobextraction image 65 extracted from the first division region 71 cannotbe measured accurately, the CPU 21 conducts the measurement of thefeature points by using the Blob extraction image 65 obtained from thesecond division region 72 whose images are captured with the highermagnification, and performs classification of the various kinds ofdefects. As described above, by capturing the high-magnification imageswhen necessary, a process after the defect detection can be performedeffectively.

When the determination of the presence of defects and classificationthereof are carried out for every defect candidate (Yes in step 111),Blob extraction images and information upon the kinds of the detecteddefects are outputted to, e.g., the display unit 26 as a detectionresult (step 112), and the process is finished. At this time, the imageprocessing PC 10 may display, for example, an image allowing one-sightrecognition of which kind of defect exists on which part of the wafer 1on the display unit 26.

Based on the outputted result, when a foreign substance is found toexist, the user removes the foreign substance. Further, when a flaw or acrack is found to exist, that protein chip 35 is discarded as anabnormal product. Further, if no defect candidate is detected in step108, the inspected protein chip 35 is determined as a normal product,and the defect detecting process is ended.

In accordance with the present embodiment, through the above-describedoperations, it becomes possible to create the model image based on theinspection target image of every first division region 71 or everysecond division region 72 even in case of the MEMS device such as theprotein chip 35 for which obtaining an absolute normal sample isdifficult. Therefore, high-accuracy defect detection is enabled.Further, since the model image 45 is created based on each inspectiontarget image 40 captured under the same optical and illuminationconditions, a wrong detection due to the difference in such conditionscan be prevented.

The present invention is not limited to the above-described embodimentbut can be modified in various ways without departing from the scope ofthe present invention.

In the above embodiment, though the protein chip is exampled as the MEMSdevice as the inspection target object, the MEMS device is not limitedthereto. For example, an electron beam irradiation plate (EB window) canbe applied as the MEMS device.

FIG. 16 illustrates an exterior view of the electron beam irradiationplate. FIG. 16( a) is a top view thereof, and FIG. 16( b) is a crosssectional view taken along the Z direction of FIG. 16( a).

As shown in FIG. 16, the electron beam irradiation plate 90 includes aplate 92 having a plurality of window holes 95 through which an electronbeam EB is to be irradiated; and a thin film 91 configured to cover thewindow holes 95.

The plate 92 is formed in a rectangular shape having an X-directionallength w and Y-directional length l of, e.g., several tens ofmillimeters and a Z-direction length h of, e.g., several millimeters.However, these lengths and shape are nothing more than examples, and thepresent invention is not limited thereto. Further, though each windowhole 95 has a rectangular shape whose one side is, e.g., severalmillimeters, this length and shape is just examples, and the window holecan have a square shape instead. Moreover, though a total of 54 (6×9)window holes 95 are formed, the number of the holes is not limitedthereto.

The electron beam irradiation plate 90 constitutes an electron beamirradiation apparatus by being connected with an end portion of anon-illustrated vacuum vessel. An electron beam EB irradiated from anelectron beam generator installed inside the vacuum vessel is irradiatedto the atmosphere through the window holes 95 and finally irradiated toa target object, as indicated by arrows in FIG. 16( b). The electronbeam irradiation apparatus is used for various purposes including, e.g.,sterilization and modification of physical property and chemicalproperty of the target object to which the electron beam is irradiated.By forming the thin film 91, the electron beam can be irradiated while avacuum state is maintained. Here, a multi-layered structure made up of aplurality of stacked thin films 91 can be used.

The electron beam irradiation plate 90 is formed on each die of thewafer 1 by an etching process using a photolithography technique or thelike, like the protein chip 35 in the above-sated embodiment. In thiscase, each die has the same size as that of the plate 92.

The defect detecting apparatus 100 performs an image pickup process, ahigh pass filtering process, a model image creating process, a Blobextracting process and so forth for the electron beam irradiation plate90, as in the case of the above-stated protein chip 35, and detects adefect such as a foreign substance, a flaw, a crack or the like presenton the electron beam irradiation plate 90. Likewise, image pickups arealso possible with low-magnification and high-magnification and atplural focal points along the Z direction. When capturing images at theplural focal points, the imaging process is performed by setting theplate 92's surface (top surface) having the thin film 91 thereon as thefirst focal position while setting the plate 92's opposite surface(bottom surface) from the thin film 91 as the second focal position.

Moreover, in the model image creating process and the Blob extractingprocess, position alignment of each inspection target image is performedalong the X, Y and θ directions such that edge shapes of the windowholes 95 shown in each inspection target image are overlapped.

Moreover, in case of a inspection of the electron beam irradiation plate90, as for feature points for the classification of defects such as athreshold value for determination of a foreign substance or the like,the image processing PC 10 creates independent feature point data basedon samples and the like of the electron beam irradiation plate 90,unlike the case of the inspection of the protein chip 35, and classifiesthe defects based on the data.

In addition, besides the protein chip 35 and the electron beamirradiation plate 90, various other MEMS devices, e.g., sensors such asan acceleration sensor, a pressure sensor, an air flow sensor and thelike, a print head of an inkjet printer, a micro mirror array of areflective projector, other kinds of actuators, various types of biochips and the like can also be applied as an inspection target object.

In the above described embodiment, though the images necessary for theimage processing, such as the inspection target images 40, the modelimages 45, the difference images 60 and the Blob extraction images 65,are stored in the HDD 25, they can be temporarily stored in the RAM 23instead or can be temporarily stored in a buffer region separate fromthe RAM 23 and deleted as soon as the defect classifying process iscompleted. Further, among the inspection target images, images fromwhich no difference is extracted through the difference extraction, thatis, images from which no defect is detected are not necessary in thefollowing process. Thus, they may be deleted in sequence from a timepoint when no detection of defect is determined. Furthermore, whencapturing the high-magnification images of the second division regions72 for the inspection target images of the first division regions 71captured with the low magnification, the inspection target images of thefirst division regions 71 become unnecessary after the image pickup ofthe second division regions 72, so that they may be deleted at a timepoint when the image pickup of the second division regions 72 iscompleted. In the above-described embodiment, since the number of thecaptured images is great, the amount of data stored in the RAM 23 or theHDD 25 can be reduced by processing as stated above, so that the load ofthe image processing PC can be reduced.

In the above-described embodiment, though the image pickup is performedat the plural focal positions by moving the XYZ stage 3 along the Zdirection, it may be also possible to capture images at the plural focalpositions by moving the CCD camera 7 along the Z direction or bypreparing a plurality of CCD cameras at each focal position.

Though the above-stated embodiment has been described for the case ofcapturing the images of each recess 50 of the protein chip 35 at thethree different focal positions, it can be also possible that the imageprocessing PC 10 measures the height of an MEMS device as an inspectiontarget in the direction of the optical axis of the CCD camera 6 and thendetermines the number of focal positions depending on the measuredheight. To elaborate, a height measuring optical interferometer, a laserscale sensor, or the like may be installed additionally. The imageprocessing PC 10 sets the focal positions in a preset interval along theheight direction depending on a range of the focal depths of the lens 14in the CCD camera 6. Accordingly, it is possible to detect defects of amicrostructure without missing any one of them regardless of the heightof the microstructure.

In the above-described embodiment, the model image is created for everyfocal position based on each inspection target image captured at thedifferent focal positions, and defects are detected by creating thedifference image and the Blob extraction image for every focal position.However, in case that a foreign substance exists at different focalpositions, for example, the user has to individually remove them byreferring to the detection result at each focal position. Thus, theimage processing PC 10 may compose the Blob extraction images for eachfocal position as a single image and output it to the display unit 26.By doing this, the user can check the defects at every focal positionwith naked eyes, and the post-process after the defect detection can beperformed more smoothly.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a configuration view of a defect detecting apparatus inaccordance with an embodiment of the present invention;

FIG. 2 sets forth a block diagram illustrating the configuration of animage processing PC in accordance with the embodiment of the presentinvention;

FIG. 3 depicts a top view of a wafer in accordance with the embodimentof the present invention;

FIG. 4 presents a top view of one of dies of the wafer in accordancewith the embodiment of the present invention;

FIG. 5 offers enlarged views of one recess portion of a protein chip inaccordance with the embodiment of the present invention;

FIG. 6 provides a flowchart to schematically describe an operationsequence of the defect detecting apparatus until it detects a defect;

FIG. 7 sets forth a diagram illustrating a device obtained by dividingeach die into a plurality of division regions in accordance with theembodiment of the present invention;

FIG. 8 depicts trajectories of image pickup positions when a CCD cameracaptures images of each division regions of the protein chip inaccordance with the embodiment of the present invention;

FIG. 9 illustrates a view of capturing inspection target images atdifferent focal positions by the CCD camera in accordance with theembodiment of the present invention;

FIG. 10 presents a flowchart to describe a detailed sequence of a highpass filtering process in accordance with the embodiment of the presentinvention;

FIG. 11 depicts a flowchart to describe a process sequence until theimage processing PC creates a model image in accordance with theembodiment of the present invention;

FIG. 12 schematically illustrates creation of the model image by theimage processing PC in accordance with the embodiment of the presentinvention;

FIG. 13 illustrates difference images before and after a Blob extractingprocess in accordance with the embodiment of the present invention;

FIG. 14 schematically illustrates a high-magnification image pickup ofeach second division region with respect to a first division region fromwhich a defect candidate is detected;

FIG. 15 provides Blob extraction images extracted from inspection targetimages of the first and second division regions in accordance with theembodiment of the present invention, respectively; and

FIG. 16 provides exterior views of an electron beam irradiation plate inaccordance with another embodiment of the present invention.

EXPLANATION OF CODES

-   1: Semiconductor wafer (wafer)-   3: XYZ stage-   4: Motor-   5: Encoder-   6: CCD camera-   7: Light source-   10: Image processing PC-   14: Lens-   21: CPU-   22: ROM-   23: RAM-   24: Input/output interface-   25: HDD-   26: Display unit-   27: Manipulation input unit-   30: Dies (semiconductor chips, chips)-   35: Protein chip-   40: Inspection target image-   45: Model image-   50: Recesses-   51: Top surface-   52: Bottom surface-   53, 91: Thin film-   55: Holes-   60: Difference image-   65: Blob extraction image-   71: First division regions-   72: Second division regions-   81: Cracks-   82: Foreign substances-   84: Noises-   90: Electron beam irradiation plate-   92: Plate-   95: Window holes-   100: Defect detecting apparatus

1. A defect detecting apparatus comprising: a measuring unit thatmeasures a height of a microstructure formed on each of a plurality ofdies on a semiconductor wafer in a vertical direction of each die; adetermining unit that determines a first focal position and a secondfocal position as at least two focal positions different from each otherin the height direction based on the measured height; an imaging unitthat captures images of the microstructure at the determined first focalposition and second focal position, with respect to every divisionregion obtained by dividing each die in plurality; a storage unit thatstores therein the images of each division region at the first andsecond focal positions together with identification information foridentifying a position of each division region within each die as afirst and a second inspection target image; a model image creating unitthat creates an average image as a first and a second model image forevery identification information, the average image being obtained byrespectively averaging, among the first and second inspection targetimages, the first and second inspection target images of respectivedivision regions having the corresponding identification informationover the dies; and a detecting unit that detects presence or absence ofa defect of the microstructure at the first and second focal positionsby extracting a difference between the first and second model images andthe first and second inspection target images corresponding to theidentification information of the first and second model images as afirst and a second difference image, respectively.
 2. The defectdetecting apparatus of claim 1, wherein the model image creating unitincludes a unit that calculates an average luminance value of everypixel included in each inspection target image having the correspondingidentification information.
 3. The defect detecting apparatus of claim1, wherein the imaging unit successively captures the images of themicrostructures on respective division regions having the correspondingidentification information over the dies.
 4. The defect detectingapparatus of claim 1, wherein after capturing the images of themicrostructures in all the division regions on one die, the imaging unitcaptures the images of the microstructures in respective divisionregions on another die adjacent to said one die.
 5. The defect detectingapparatus of claim 1, further comprising: a unit that generates andoutputs a composite difference image by composing the first and seconddifference images as a single image.
 6. The defect detecting apparatusof claim 1, wherein the microstructures are screening test vesselsincluding: a plurality of recesses each having a thin film shaped bottomsurface and introducing therein a reagent and an antibody whichcross-reacts with the reagent; and a plurality of holes provided in thebottom surface of each recess to discharge out the reagent which doesnot react with the antibody.
 7. The defect detecting apparatus of claim6, wherein prior to respectively averaging of the first and secondinspection images corresponding to the identification information of thefirst and second model images, the model image creating unit includes aunit configured to respectively align positions of the first and secondinspection target images based on a shape of each recess of the vesselon the first and second inspection target images.
 8. The defectdetecting apparatus of claim 6, wherein the vessel has a top surfacedistanced from the bottom surface by a preset interval, and the secondfocal position includes a third focal position and a fourth focalposition different from the third focal position, and the imaging unitsets the top surface of the vessel as the first focal position, thebottom surface as the third focal position and a preset position betweenthe top and bottom surfaces as the fourth focal position.
 9. The defectdetecting apparatus of claim 1, wherein the microstructure is anelectron beam irradiation plate including a plate member having aplurality of window holes for irradiating electron beams and a thin filmprovided to cover each window hole.
 10. The defect detecting apparatusof claim 9, wherein prior to respectively averaging of the first andsecond inspection images corresponding to the identification informationof the first and second model images, the model image creating unitincludes a unit that respectively aligns positions of the first andsecond inspection target images based on a shape of each window hole ofthe electron beam irradiation plate on the first and second inspectiontarget images.
 11. The defect detecting apparatus of claim 9, whereinthe electron beam irradiation plate has a top surface on a side wherethe thin film is formed and a bottom surface facing the top surface witha preset interval maintained therebetween, and the imaging unit capturesan image of the electron beam irradiation plate by setting the topsurface as the first focal position and the bottom surface as the secondfocal position.
 12. A defect detecting method comprising: measuring aheight of a microstructure formed on each of a plurality of dies on asemiconductor wafer in a vertical direction of each die; determining, afirst focal position and a second focal position as at least two focalpositions different from each other in the height direction based on themeasured height; capturing images of the microstructure at thedetermined first focal position and second focal position, with respectto every division region obtained by dividing each die in plurality;storing the images of each division region at the first and second focalpositions together with identification information for identifying aposition of each division region within each die as a first and a secondinspection target image; creating an average image as a first and asecond model image for every identification information, the averageimage being obtained by respectively averaging, among the first andsecond inspection target images, the first and second inspection targetimages of respective division regions having the correspondingidentification information over the dies; and detecting presence orabsence of a defect of the microstructure at the first and second focalpositions by extracting a difference between the first and second modelimages and the first and second inspection target images correspondingto the identification information of the first and second model imagesas a first and a second difference image, respectively, wherein thesteps of the method are performed by an apparatus detecting defects on asemiconductor wafer.
 13. An information processing apparatus comprising:a determining unit that determines, based on a height of amicrostructure formed on each of a plurality of dies on a semiconductorwafer, a first focal position and a second focal position as at leasttwo focal positions different from each other in a height direction, theheight of the microstructure being measured in the vertical direction ofeach die; a storage unit that stores therein images of a themicrostructure as a first and a second inspection target image, theimages being captured at the determined first focal position and secondfocal position with respect to every division region obtained bydividing each die in plurality, together with identification informationfor identifying a position of each division region within each die; amodel image creating unit that creates an average image as a first and asecond model image for every identification information, the averageimage being obtained by respectively averaging, among the first andsecond inspection target images, the first and second inspection targetimages of respective division regions having the correspondingidentification information over the dies; and a detecting unit thatdetects presence or absence of a defect of the microstructure at thefirst and second focal positions by extracting a difference between thefirst and second model images and the first and second inspection targetimages corresponding to the identification information of the first andsecond model images as a first and a second difference image,respectively.
 14. An information processing method comprising:determining, based on a height of a microstructure formed on each of aplurality of dies on a semiconductor wafer, a first focal position and asecond focal position as at least two focal positions different fromeach other in a height direction, the height of the microstructure beingmeasured in the vertical direction of each die; storing images of themicrostructure as a first and a second inspection target image, theimages being captured at the determined first focal position and secondfocal position with respect to every division region obtained bydividing each die in plurality, together with identification informationfor identifying a position of each division region within each die;creating an average image as a first and a second model image for everyidentification information, the average image being obtained byrespectively averaging, among the first and second inspection targetimages, the first and second inspection target images of respectivedivision regions having the corresponding identification informationover the dies; and detecting presence or absence of a defect of themicrostructure at the first and second focal positions by extracting adifference between the first and second model images and the first andsecond inspection target images corresponding to the identificationinformation of the first and second model images as a first and a seconddifference image, respectively, wherein the steps of the method areperformed by an apparatus detecting defect on a semiconductor wafer. 15.A non-transitory computer-readable medium storing a program forexecuting, in an information processing apparatus, the processes of:determining, based on a height of a microstructure formed on each of aplurality of dies on a semiconductor wafer, a first focal position and asecond focal position as at least two focal positions different fromeach other in a height direction, the height of the microstructure beingmeasured in the vertical direction of each die; storing images of themicrostructure as a first and a second inspection target image, theimages being captured at the determined first focal position and secondfocal position with respect to every division region obtained bydividing each die in plurality, together with identification informationfor identifying a position of each division region within each die;creating an average image as a first and a second model image for everyidentification information, the average image being obtained byrespectively averaging, among the first and second inspection targetimages, the first and second inspection target images of respectivedivision regions having the corresponding identification informationover the dies; and detecting presence or absence of a defect of themicrostructure at the first and second focal positions by extracting adifference between the first and second model images and the first andsecond inspection target images corresponding to the identificationinformation of the first and second model images as a first and a seconddifference image, respectively.